8,143 research outputs found
Fuzzy rule-based system applied to risk estimation of cardiovascular patients
Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. Š 2013 Old City Publishing, Inc
Using privileged information to manipulate markets: insiders, gurus, and credibility
Access to private information is shown to generate both the incentives and the ability to manipulate asset markets through strategically distorted announcements. The fact that privileged information is noisy interferes with the public's attempts to learn whether such announcements are honest; it allows opportunistic individuals to manipulate prices repeatedly, without ever being fully found out. This leads us to extend Sobel's [1985] model of strategic communication to the case of noisy private signals. Our results show that when truthfulness is not easily verifiable, restrictions on trading by insiders may be needed to preserve the integrity of information embodied in prices
College admissions and the role of information : an experimental study
We analyze two well-known matching mechanismsâthe Gale-Shapley, and the Top
Trading Cycles (TTC) mechanismsâin the experimental lab in three different informational
settings, and study the role of information in individual decision making. Our results suggest
thatâin line with the theoryâin the college admissions model the Gale-Shapley mechanism
outperforms the TTC mechanisms in terms of efficiency and stability, and it is as successful as
the TTC mechanism regarding the proportion of truthful preference revelation. In addition, we
find that information has an important effect on truthful behavior and stability. Nevertheless,
regarding efficiency, the Gale-Shapley mechanism is less sensitive to the amount of information
participants hold
Debiased-CAM for bias-agnostic faithful visual explanations of deep convolutional networks
Class activation maps (CAMs) explain convolutional neural network predictions
by identifying salient pixels, but they become misaligned and misleading when
explaining predictions on images under bias, such as images blurred
accidentally or deliberately for privacy protection, or images with improper
white balance. Despite model fine-tuning to improve prediction performance on
these biased images, we demonstrate that CAM explanations become more deviated
and unfaithful with increased image bias. We present Debiased-CAM to recover
explanation faithfulness across various bias types and levels by training a
multi-input, multi-task model with auxiliary tasks for CAM and bias level
predictions. With CAM as a prediction task, explanations are made tunable by
retraining the main model layers and made faithful by self-supervised learning
from CAMs of unbiased images. The model provides representative, bias-agnostic
CAM explanations about the predictions on biased images as if generated from
their unbiased form. In four simulation studies with different biases and
prediction tasks, Debiased-CAM improved both CAM faithfulness and task
performance. We further conducted two controlled user studies to validate its
truthfulness and helpfulness, respectively. Quantitative and qualitative
analyses of participant responses confirmed Debiased-CAM as more truthful and
helpful. Debiased-CAM thus provides a basis to generate more faithful and
relevant explanations for a wide range of real-world applications with various
sources of bias
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
CHI â22, April 29-May 5, 2022, New Orleans, LA, USA Š 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9157-3/22/04. https://doi.org/10.1145/3491102.3517522Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when explaining predictions of images that are subject to systematic error (bias). Furthermore, the distortions persist despite model fine-tuning on images biased by different factors (blur, color temperature, day/night). We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions. In simulation studies, the approach not only enhanced prediction accuracy, but also generated highly faithful explanations about these predictions as if the images were unbiased. In user studies, debiased explanations improved user task performance, perceived truthfulness and perceived helpfulness. Debiased training can provide a versatile platform for robust performance and explanation faithfulness for a wide range of applications with data biases.Peer ReviewedPostprint (published version
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A tree formulation for signaling games
We provide a detailed presentation and complete analysis of the sender/receiver Lewis signaling game using a game theory extensive form, decision tree formulation. The analysis employs well established game theory ideas and concepts. We establish the existence of four perfect Bayesian equilibria in this game. We explain which equilibrium is the most likely to prevail. Our explanation provides an essential step for understanding the formation of a language convention. Further, we discuss the informational content of such signals and calibrate a more detailed definition of a true (âcorrectâ) signal in terms of the payoffs of the sender and the receiver
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